Towards Empowering Educators to Create their own Smart Personal Assistants

Despite a growing body of research about the design and use of Smart Personal Assistants such as Amazon’s Alexa or Google’s Assistant, little is known about their ability to help educators offering individual support in large-scale learning environments. Smart Personal Assistant ecosystems empower educators to develop their own agents without deep technological knowledge. The objective of this paper is to design and validate a method that helps educators to create Smart Personal Assistants as learning tutors. Using a design science research approach, we first gather requirements from students and educators as well as from information systems and education theory. Next, we create an alpha version of our method and evaluate it with a focus group before we instantiate our artifact in an everyday learning environment. The findings indicate that our method is able to empower educators to design Smart Personal Assistants that significantly improve students’ learning success

Bringing AI into the Classroom: Designing Smart Personal Assistants as Learning Tutors

Despite a growing body of research about the design and use of Smart Personal Assistants like Amazon’s Alexa, little is known about their ability to be learning tutors. New emerging ecosystems empower educators to develop their own agents without deep technical knowledge. The objective of our study is to find and validate a general set of design principles that educators can use to design their own agents. Using a design science research approach, we present requirements from students and educators as well as IS and education theory. Next, we formulate design principles and evaluate them with a focus group before we instantiate our artifact in an everyday learning environment. The findings of this short paper indicate that the design principles and corresponding artifact are able to significantly improve learning outcomes. The completed work aims to develop a nascent design theory for designing Smart Personal Assistants as learning tutors

Alexa, Can You Help Me Solve That Problem? – Understanding the Value of Smart Personal Assistants as Tutors for Complex Problem Tasks

In recent decades, the number of students per lecturer at universities has constantly risen. In these learning scenarios, individual lecturer support for helping students actively acquiring new knowledge is hardly possible. However, active student behavior is necessary for successful learning. Smart Personal Assistants such as Amazon’s Alexa or Google’s Home promise to fill this gap by being students’ individual tutors. In order to understand what students expect from Smart Personal Assistants as tutors and how they interact with them, we will carry out an experiment. In this research in progress paper, we present our experiment design, where we observe the individual interaction between students and a Smart Personal Assistant tutor and between students and a human tutor applying the same methods in both cases. Drawing on the concepts of parasocial interaction and trust, we derive hypotheses, present the Smart Personal Assistant development and explain the experiment process in detail

SPAM – A Process Model for Developing Smart Personal Assistants

Information technology capabilities are growing at an impressive pace and increasingly overstrain the cognitive abilities of users. User assistance systems such as online manuals try to help the user in handling these systems. However, there is strong evidence that traditional user assistance systems are not as effective as intended. With the rise of smart personal assistants, such as Amazon’s Alexa, user assistance systems are becoming more sophisticated by offering a higher degree of interaction and intelligence. This study proposes a process model to develop Smart Personal Assistants. Using a design science research approach, we first gather requirements from Smart Personal Assistant designers and theory, and later evaluate the process model with developing an Amazon Alexa Skill for a Smart Home system. This paper contributes to the existing user assistance literature by offering a new process model on how to design Smart Personal Assistants for intelligent systems

Tetrakis(diisopropyl amide) substituted norbornadiene and quadricyclane are highly barium selective ligands

Tetrakis(diisopropyl amide) substituted norbornadiene and quadricyclane derivatives were investigated for their extraction and transport capabilities with alkaline earth metal cations. Both amides exhibited a remarkably high preference of Ba2+ over any other alkali metal or alkaline earth cation. The binding geometries were determined by quantum chemical DFT calculations

Towards a Technique for Modeling New Forms of Collaborative Work Practices – The Facilitation Process Model 2.0

Collaboration Engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by practitioners themselves without the ongoing support of external collaboration professionals. A key design activity in CE concerns modeling current and future collaborative work practices. CE researchers and practitioners have used the Facilitation Process Model (FPM) technique. However, this modeling technique suffers from a number of shortcomings to model contemporary collaborative work practices. We use a design science approach to identify the main challenges with the original FPM technique, derive requirements and design a revised modeling technique that is based on the current technique enriched by BPMN 2.0 elements. This paper contributes to the CE literature by offering a revised FPM technique that assists CE-designers to capture new forms of collaborative work practices

Photon pressure induced test mass deformation in gravitational-wave detectors

A widely used assumption within the gravitational-wave community has so far been that a test mass acts like a rigid body for frequencies in the detection band, i.e. for frequencies far below the first internal resonance. In this article we demonstrate that localized forces, applied for example by a photon pressure actuator, can result in a non-negligible elastic deformation of the test masses. For a photon pressure actuator setup used in the gravitational wave detector GEO600 we measured that this effect modifies the standard response function by 10% at 1 kHz and about 100% at 2.5 kHz